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        <h1 id="一、研究背景"><a href="#一、研究背景" class="headerlink" title="一、研究背景"></a>一、研究背景</h1><p>2020年全国大学生电子设计大赛中，A题中有一个小项目为步数检测，这让我开始了计步算法的探究。通过查阅文献，最后选择陈国良团队《一种基于自适应波峰检测的MEMS计步算法》一文中提及的波峰检测算法为基础思路，编写了基础的步数检测代码。</p>
<h1 id="二、算法实现"><a href="#二、算法实现" class="headerlink" title="二、算法实现"></a>二、算法实现</h1><h3 id="1-基础思路"><a href="#1-基础思路" class="headerlink" title="1. 基础思路"></a>1. 基础思路</h3><p>通过对人体行走特征进行分析，人行走频率一般在1Hz到2.5Hz 范围内，跑步时频率不超过 5Hz，加速度在0.2g~2g之间。由于跑动时步频较大，15 Hz 和20 Hz 的加速度采样频率无法完整地记录步态信息，本算法选取 50 Hz 采样频率采集加速度数据 。由于人在行走时，空间加速度会有明显的波峰出现，则可以通过波峰检测来读取步数。</p>
<p>首先选取一定范围的采样点，如20个采样点来取最大值，找出潜在波峰。</p>
<p>而真实波峰检测上为避免伪波峰被误检测，需要经过3次滤波：</p>
<ol>
<li><p>行走时，总加速度值会大于某个数值。大于该数值则有可能为真实波峰。当然，考虑到算法可移植性，不同的加速度传感器的加速度值可能不同，则移植到新的硬件平台上需要重新标定阈值。</p>
</li>
<li><p>人正常行走的时间是在一定频率范围内的，论文中指出时间差满足时间阈值[0.3 s, 0.8 s]为正常态。则将潜在峰值与前一波峰比较，如果时间在该范围内，则为正常行走时间间隔。</p>
</li>
<li><p>将潜在波峰与前后相邻10个数据点进行比较，若潜在波峰仍为最大值，则该波峰为真实波峰，否则，退出检测。</p>
<p><img src="https://img-blog.csdnimg.cn/20201018165416280.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMwNDQ3MzE1,size_16,color_FFFFFF,t_70#pic_center" alt="算法流程图"></p>
</li>
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<h3 id="2-算法难点及注意事项"><a href="#2-算法难点及注意事项" class="headerlink" title="2. 算法难点及注意事项"></a>2. 算法难点及注意事项</h3><p>（1）不同传感器因为自身性能原因，需要解决陀螺仪传感器的数据漂移。<br><strong>解决方案</strong>：将传感器传回来的x，y，z轴加速度数据平方求和，减小单个方向加速度数据产生的误差。</p>
<p>（2）要进行波峰检测，需要确保数据实时传入且存储，需要用<strong>队列</strong>这一数据结构来存储数据，确保数据的时间顺序不出差错。<br><strong>解决方案</strong>：构建队列存储、读取数据，实现FIFO(先进先出)功能。</p>
<h3 id="3-总体代码思路"><a href="#3-总体代码思路" class="headerlink" title="3.总体代码思路"></a>3.总体代码思路</h3><figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br></pre></td><td class="code"><pre><span class="line"><span class="type">uint16_t</span> WALK_COUNT=<span class="number">0</span>;<span class="comment">//初始步数记为0</span></span><br><span class="line"><span class="comment">//uint16_t Real_Peak_position=0;//真实波峰的时间节点</span></span><br><span class="line"><span class="class"><span class="keyword">struct</span> <span class="title">max_info</span> <span class="title">maxvalue</span>[2];</span><span class="comment">//至少要3个最大值，最大值均需与前后两个波峰比较,人为设置0为前一值，1为现在值，2为后一值</span></span><br><span class="line"><span class="type">uint8_t</span> value_right=<span class="number">0</span>,time_right=<span class="number">0</span>,before_after_right=<span class="number">0</span>;</span><br><span class="line"><span class="type">float</span> walk_distance=<span class="number">0</span>;</span><br><span class="line"><span class="type">void</span> <span class="title function_">walk_step</span><span class="params">(<span class="type">void</span>)</span></span><br><span class="line">&#123;</span><br><span class="line">    data_get_once();</span><br><span class="line"></span><br><span class="line">    maxvalue[<span class="number">1</span>]=find_max(a,<span class="number">20</span>);<span class="comment">//找到20个历元的滑动窗口中的潜在峰值</span></span><br><span class="line">    <span class="keyword">if</span>((maxvalue[<span class="number">1</span>].value&gt;MIN_G_SPEED)&amp;&amp;(maxvalue[<span class="number">1</span>].value&lt;MAX_G_SPEED))</span><br><span class="line">    &#123;</span><br><span class="line">         value_right=<span class="number">1</span>;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(time_judge(maxvalue[<span class="number">1</span>].position,maxvalue[<span class="number">0</span>].position))</span><br><span class="line">    &#123;</span><br><span class="line">        time_right=<span class="number">1</span>;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(before_after_judge(maxvalue[<span class="number">1</span>].value,before,after))</span><br><span class="line">    &#123;</span><br><span class="line">        before_after_right=<span class="number">1</span>;</span><br><span class="line">    &#125;</span><br><span class="line">	time_right=<span class="number">1</span>;<span class="comment">//我的算法中未采用时间间隔滤波，因为实际测量可能会出现丢步的情况</span></span><br><span class="line">    <span class="keyword">if</span>(value_right&amp;&amp;time_right&amp;&amp;before_after_right)</span><br><span class="line">    &#123;</span><br><span class="line">        WALK_COUNT++;</span><br><span class="line">        maxvalue[<span class="number">0</span>].value=maxvalue[<span class="number">1</span>].value;<span class="comment">//更新上一波峰值，为下一次波峰比对做准备</span></span><br><span class="line">        maxvalue[<span class="number">0</span>].position=maxvalue[<span class="number">1</span>].position; </span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">else</span><span class="comment">//如果不符合要求</span></span><br><span class="line">    &#123;</span><br><span class="line">        maxvalue[<span class="number">1</span>].position+=<span class="number">20</span>;<span class="comment">//上一个波峰位置加20</span></span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">		value_right=<span class="number">0</span>;</span><br><span class="line">		time_right=<span class="number">0</span>;</span><br><span class="line">		before_after_right=<span class="number">0</span>;</span><br><span class="line">		</span><br><span class="line">		walk_distance=WALK_COUNT*<span class="number">0.60</span>;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>

<h3 id="4-硬件传感器"><a href="#4-硬件传感器" class="headerlink" title="4. 硬件传感器"></a>4. 硬件传感器</h3><p>加速度传感器选用正点原子ATK-IMU901模块，设置采样频率为50hz。</p>
<h1 id="三、总结"><a href="#三、总结" class="headerlink" title="三、总结"></a>三、总结</h1><p>算法总体思路清晰简介，能实际应用再测算步数的最主要原因是，硬件结构即陀螺仪的数据稳定。若使用噪声大，数据易漂移传感器，如MPU6050，应进行数据滤波使数据保持稳定再来考虑算法实现。如采用进行DMP结算或者原始数据读取卡尔曼滤波等。</p>

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